The promise of implementing machine learning in earthquake engineering: A state-of-the-art review
Machine learning (ML) has evolved rapidly over recent years with the promise to
substantially alter and enhance the role of data science in a variety of disciplines. Compared …
substantially alter and enhance the role of data science in a variety of disciplines. Compared …
Regional seismic risk and resilience assessment: Methodological development, applicability, and future research needs–An earthquake engineering perspective
Given the devastating losses incurred by past major earthquake events together with the
ever-increasing global seismic exposures due to population growth and urbanization …
ever-increasing global seismic exposures due to population growth and urbanization …
Failure mode and effects analysis of RC members based on machine-learning-based SHapley Additive exPlanations (SHAP) approach
Abstract Machine learning approaches can establish the complex and non-linear
relationship among input and response variables for the seismic damage assessment of …
relationship among input and response variables for the seismic damage assessment of …
Data-driven shear strength prediction of steel fiber reinforced concrete beams using machine learning approach
The incorporation of steel fibers in a concrete mix enhances the shear capacity of reinforced
concrete beams and a comprehensive understanding of this phenomenon is imperative to …
concrete beams and a comprehensive understanding of this phenomenon is imperative to …
Data-driven machine-learning-based seismic failure mode identification of reinforced concrete shear walls
A reinforced concrete shear wall is one of the most critical structural members in buildings, in
terms of carrying lateral loads. Despite its importance, post-earthquake reconnaissance and …
terms of carrying lateral loads. Despite its importance, post-earthquake reconnaissance and …
Machine learning-based approaches for seismic demand and collapse of ductile reinforced concrete building frames
Robust seismic vulnerability assessment for a building under expected earthquake ground
motions necessitates explicit consideration of all-important sources of uncertainty in …
motions necessitates explicit consideration of all-important sources of uncertainty in …
Machine-learning interpretability techniques for seismic performance assessment of infrastructure systems
Abstract Machine-learning has recently gained considerable attention in the earthquake
engineering community, as it can map the complex relationship between the expected …
engineering community, as it can map the complex relationship between the expected …
Explainable machine learning models for punching shear strength estimation of flat slabs without transverse reinforcement
Flat slabs, despite their aesthetic qualities and widespread use in construction, are
susceptible to brittle shear failure. In addition, although design provisions are available, they …
susceptible to brittle shear failure. In addition, although design provisions are available, they …
Artificial neural network based multi-dimensional fragility development of skewed concrete bridge classes
Recent researches are directed towards the regional seismic risk assessment of structures
based on a bridge inventory analysis. The framework for traditional regional risk …
based on a bridge inventory analysis. The framework for traditional regional risk …
Rapid seismic damage evaluation of bridge portfolios using machine learning techniques
The damage state of a bridge has significant implications on the post-earthquake
emergency traffic and recovery operations and is critical to identify the post-earthquake …
emergency traffic and recovery operations and is critical to identify the post-earthquake …